The systems and methods disclosed relate generally to video processing and more particularly to adaptively compressing video based on video analytics.
Surveillance technology has been increasingly used to monitor people, places and activities. For example, high-quality surveillance video is being used to better monitor events and/or to reduce visually distracting artifacts that may interfere with human recognition. As surveillance video data is retained and archived for longer periods of time, large amounts of data storage space are typically needed. In addition, more innovative applications are emerging in which the streaming of video to wireless and mobile devices is used over evermore bandwidth-constrained networks. Such uses are demanding not only new surveillance solutions, but also new or enhanced video compression techniques.
To address the above needs for enhanced compression, it is desirable to have a technique of coding objects in the surveillance scene so that a region-of-interest (ROI) can be compressed at higher quality relative to other regions that are visually less-important such as the scene background, for example. While such techniques have been proposed, they require the use of custom encoders and decoders. The widespread use of video makes the deployment of such devices complicated and expensive; a more desirable solution would be one that permits compressed video streams to be decoded by industry-standard decoders without requiring special plug-ins or customization. It is furthermore desirable to have an encoder that produces bit streams that are compliant with the MPEG-4 or H.264 compression standards. Within these standards, it is also desirable to selectively allocate bits to portions of the scene that are deemed to be important; scene analysis using video analytics (also called “video content analysis”) can be a powerful tool for performing this function.